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Transactive Memory
Systems 1985–2010: An
Integrative Framework of Key
Dimensions, Antecedents, and
Consequences
Yuqing Ren a & Linda Argote b
a Carlson School of Management , University of
Minnesota
b Tepper School of Business , Carnegie Mellon
University
Published online: 26 Jul 2011.
To cite this article: Yuqing Ren & Linda Argote (2011)
Transactive Memory
Systems 1985–2010: An Integrative Framework of Key
Dimensions, Antecedents,
and Consequences, The Academy of Management Annals, 5:1,

189-229, DOI:
10.1080/19416520.2011.590300
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Transactive Memory Systems 1985 – 2010:

An Integrative Framework of Key Dimensions, Antecedents,
and Consequences
YUQING REN∗
Carlson School of Management, University of Minnesota
LINDA ARGOTE
Tepper School of Business, Carnegie Mellon University
Abstract
Over two decades have passed since Wegner and his co-authors
published the
groundbreaking paper on transactive memory systems (TMS) in
1985. The
concept attracted the interest of management, psychology, and
communication
scholars who have employed a variety of methods to examine
the phenomenon.
In this paper, we review 76 papers that examined transactive
memory systems
and summarize the findings in an integrative framework to show
the antecedents
and consequences of TMS. Our review also reveals important
issues in the litera-
ture related to the measurement of TMS, its multidimensional
nature, extending
TMS from the team level to the organizational level, and the
potential role of
TMS in explaining the benefits of experience in existing
organizations and
∗ Corresponding author. Email:[email protected]

The Academy of Management Annals
Vol. 5, No. 1, June 2011, 189 – 229
ISSN 1941-6520 print/ISSN 1941-6067 online
# 2011 Academy of Management
DOI: 10.1080/19416520.2011.590300
http://www.informaworld.com
189
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new entrepreneurial ventures. We conclude by calling for future
research to
examine the dynamic evolution of TMS, TMS in virtual teams,
TMS in entrepre-
neurial ventures, and TMS at the organizational level facilitated
with information
technologies.

Introduction
Over two decades have passed since Wegner and his
collaborators first pre-
sented the concept of transactive memory to refer to a collective
system that
individuals in close relationships use to encode, store, and
retrieve knowledge
(Wegner, Giuliano, & Hertel, 1985; Wegner, 1987). Over the
past two decades,
researchers from many disciplines including communication,
management,
social psychology, and information systems have become
interested the
concept and its effects in group and organizational settings. The
concept has
been extended beyond its original context of collective
remembering in inti-
mate couples to work groups and organizations (e.g., Liang,
Moreland, &
Argote, 1995; Jackson & Klobas, 2008; Lewis, 2003). We have
also accumulated
a large body of literature regarding the antecedents and
consequences of trans-
active memory in dyads and work groups. Much of the insight,
nonetheless,
remains dispersed in separate studies with limited integration.
In this article, we report findings from a comprehensive review
of the transac-
tive memory literature. We summarize the literature in an
integrative framework
that shows the antecedents, consequences, and factors that
moderate the relation-
ship between transactive memory systems (TMS) and various

outcomes. Our
review suggests four important issues that need to be considered
and addressed
in future research in terms of (1) using standard scales in
measuring transactive
memory systems, (2) deciphering the relationships among the
dimensions of
transactive memory systems, (3) extending transactive memory
systems to the
organizational level, and (4) investigating the role of TMS in
explaining the
benefits of experience in existing organizations and new
entrepreneurial ventures.
Three observations motivated our decision to conduct a
comprehensive
review of the literature. First, despite two decades of research,
the transactive
memory literature remains somewhat fragmented. Researchers
choose to
study variables of their individual interest and there is little
systematic inte-
gration such as the input – process – output framework of team
effectiveness
(Mathieu, Maynard, Rapp, & Gilson, 2008). Although many
researchers have
examined factors of great interest—such as stress (Ellis, 2006),
gender stereo-
types (Hollingshead & Fraidin, 2003), and communication
(Lewis, 2004), to
name just a few—a review is likely to help us integrate
accumulated knowledge
and identify gaps in the literature for future research.
Second, the growth of the literature increases the likelihood of
replicating

studies and cross-checking the effects of variables across
studies. At the same
time, we begin to observe relationships that are inconsistent or
even contradict
190 † The Academy of Management Annals
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one another across studies. One example is how the relationship
between trust
and transactive memory is conceived. Kanawattanachai and Yoo
(2007)
measured cognition-based trust as a key dimension of
transactive memory,
similar to the task credibility dimension in Lewis’s 15-item
scale (2003). In con-
trast, Akgun, Byrne, and Kesin (2005) examined trust as an
antecedent to

transactive memory systems, whereas Rau (2005) examined
trust as a modera-
tor of the relationship between transactive memory systems and
top manage-
ment team performance. A review is likely to help us cross
validate insights
from different studies and begin to reconcile inconsistencies in
the relation-
ships between TMS and other constructs.
Third, we hope the review will serve as a vehicle to bridge the
gap between
theory and practice. While the benefits of TMS have been
consistently demon-
strated in small groups, there is not much evidence showing its
working and
benefits in organizations at large, although many organizations
have invested
heavily in technological solutions to make better use of their
intellectual capital
(Moreland & Argote, 2003). Part of the answer may reside in
the context of the
studies that linked TMS to improved performance and the extent
to which
these findings generalize beyond small face-to-face groups. A
comprehensive
review allows us to compare findings across methods and
contexts and thereby
to identify consistencies and inconsistencies in the literature.
In the rest of the article, we first define the concept of
transactive memory
and present a representation of its key elements. We then
describe how we
selected the articles to review, together with basic statistics
about the literature.

We summarize our findings in an integrative framework about
the theoretical
antecedents and consequences of transactive memory on group
outcomes
together with factors that moderate the relationship between
transactive
memory systems and outcomes. We conclude with a discussion
of important
issues that need to be addressed and our recommendations for
future research.
Transactive Memory Systems Defined
The concept of transactive memory systems was first introduced
as a mechan-
ism to illustrate how individuals can rely upon external aids
such as books, arti-
facts, or group members to extend individual memory. Wegner,
Giuliano, and
Hertel (1985) defined the two components of transactive
memory as: (1) orga-
nized knowledge contained entirely in the individual memory
systems of group
members, and (2) a set of transactive processes that occur
among group
members. A TMS consists of a set of individual memory
systems in combi-
nation with the communication that takes place between
individuals
(Wegner, 1987). A commonly used definition of transactive
memory system
is a shared system that people in relationships develop for
encoding, storing,
and retrieving information about different substantive domains
(Hollingshead,
1998a; Ren, Carley, & Argote, 2006).

Transactive Memory Systems 1985 – 2010 † 191
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A common confusion in the literature is to equate transactive
memory with
transactive memory systems and use the terms interchangeably.
The two con-
cepts are distinct, with transactive memory being a component
of a TMS. As
Wegner, Giuliano and Hertel (1985) indicated, a TMS has two
components:
a structural component, which shows how transactive memory
links individual
memories to a collective knowledge network; and three
transactive processes
that can occur during the encoding, storing, and retrieval of
information in

the group memory. An individual’s memory has two
components: an individ-
ual memory of information the individual possesses, and a
transactive memory
with label and location information about what other members
know. The
second part is also referred to as metaknowledge, which helps
individuals to
retrieve information from external sources. Although a
transactive memory
or metaknowledge provides hints about “who knows what,” a
TMS goes
beyond the simple presence of the knowledge and requires
dyads or groups
to also engage in transactive processes. In 1995, Wegner
illustrated the
concept using the computer network metaphor and identified
three processes
related to the development and application of transactive
memory systems:
directory updating (people learn what others are likely to
know); information
allocation (new information is communicated to the person
whose expertise
will facilitate its storage); and retrieval coordination
(information on any
topic is retrieved based on knowledge of the relative expertise
of the individuals
in the memory system).
Liang, Moreland, and Argote (1995) extended the TMS concept
to the
group level of analysis. They identified three indicators or
symptoms of
groups with established transactive memory systems by
analyzing videotapes

of groups performing a radio assembly task. These investigators
found that,
compared with groups whose members were trained
individually, groups
whose members were trained together tended to specialize in
remembering
different aspects of the assembly task, to trust each other’s
expertise, and to
coordinate their activities more effectively. These indicators
were respectively
named memory differentiation, task credibility, and task
coordination. Based
on this study, Lewis (2003) developed a 15-item scale that has
been widely
used to measure the presence of transactive memory systems in
laboratory
and field research.
The concept of transactive memory systems is similar to but
distinct from
related concepts such as team mental models (Klimoski &
Mohammed, 1994),
shared-task understanding (He, Butler, & King, 2007) and
cross-understanding
(Huber & Lewis, 2010). Team mental models have been defined
as shared
mental representations of aspects of a team’s task environment
(Klimoski &
Mohammed, 1994). The concept of shared-task understanding,
which is
closely related to the concept of shared mental models, refers to
knowledge
structures shared by team members about the team’s task, goals,
strategies
and so on (He et al., 2007). The concept of cross-understanding
refers to the

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extent to which group members have an accurate understanding
of the mental
models of other group members (Huber & Lewis, 2010). What
all of these con-
cepts have in common with the concept of transactive memory
is that they
imply or capture cognitive representations shared among team
members.
Transactive memory systems differ from these other concepts in
two important
ways. First, transactive memory systems are narrower in content
coverage than
the other concepts. Transactive memory systems include
knowledge about who

knows what, while team mental models and shared-task
understandings
include additional content such as team goals and strategies,
and cross-under-
standing includes member’s beliefs and preferences. Second and
most impor-
tantly, a TMS is a cooperative division of labor for learning,
remembering and
communicating knowledge (Wegner, 1987). Thus, groups with a
well-devel-
oped TMS exhibit differentiation where different members
specialize in learn-
ing, remembering and sharing different knowledge. By contrast,
team mental
models, shared-task understandings and cross-understandings do
not
involve a differentiated memory structure or a division of labor.
Thus, in con-
trast to the other concepts, transactive memory systems involve
a differentiated
structure where members specialize in remembering knowledge
from different
domains. A TMS is a retention bin or knowledge repository
(Argote & Ingram,
2000) for group or organizational memory (Walsh & Ungson,
1991). A TMS
stores knowledge of who knows what, which can influence the
future perform-
ance of the group or organization (Argote & Miron-Spektor, in
press; Levitt &
March, 1988).
Literature Search and Review
We searched for research articles in the Web of Science
database with the

keyword “transactive memory” in the title, abstract, or list of
keywords of
the paper in three citation databases: Science Citation Index
Expanded,
Social Sciences Citation Index, and Arts & Humanities Citation
Index.
Altogether, we found 206 papers. We included papers with a
primary focus
on transactive memory systems, meaning that TMS was included
as an inde-
pendent variable, a dependent variable, or a mediating variable.
We excluded
papers that examined TMS only as a moderating variable
because incorporat-
ing them in our theoretical framework would require the
inclusion of the two
variables whose relationship is moderated by TMS, and thereby
make our
theoretical framework more cumbersome. One example is a
study by Espinosa,
Slaughter, Kraut, and Herbsleb (2007) that investigated three
types of coordi-
nation needs in software teams—technical, process, and
temporal—and how
those needs were negatively affected by geographic distance.
Transactive
memory was examined as a type of shared knowledge that
mitigated the nega-
tive effects of geographic distance on team coordination needs.
Including such
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studies would require adding variables such as coordination
needs to our
theoretical framework, which would compromise its
parsimonious structure.
Figure 1 shows the number of papers published each year, with
the bottom
portion being the ones included in our review. We did not
include years before
1999 because the publication of TMS papers remained sparse.
Interest in trans-
active memory has accelerated in the past 10 years. At the same
time, the pro-
portion of papers that refer to transactive memory systems but
in which TMS is
not the core topic of the paper has also grown significantly.
These are signs that
transactive memory systems are being reified, as with other
well-known con-
cepts such as absorptive capacity (Lane, Koka, & Pathak, 2006).

In other
words, as more people become familiar with the concept, they
begin to “take
it for granted” and use it conveniently to meet individual
research needs,
with little consideration of the original assumptions and
propositions that
define the relationships of the construct with other constructs.
Figure 2 shows the distribution of studies using different
research methods.
Of the 76 studies reviewed, 18 are conceptual or review papers,
25 are exper-
imental research, 31 are field studies, and two are simulations
(Palazzolo,
Figure 1 Transactive Memory Papers Published between 1985
and 2010.
Figure 2 Review of Types of Paper Published between 1985 and
2010.
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Serb, She, Su, & Contractor, 2006; Ren et al., 2006). Two-thirds
of the field
studies were published in or after 2006. Of the 31 field studies,
22 surveyed
work groups in a variety of real-world organizations including
banks, hospitals,
manufacturing firms, and high-tech or R&D institutes. Thus, the
empirical
work is divided almost equally between the laboratory and field.
Figure 3 shows the distribution of studies across different levels
of analysis.
More than three-quarters of the studies we reviewed examined
transactive
memory systems at the team or group level (59 or 77.6%).
Another 10
studies examined transactive memory systems at the dyadic
level. Three
studies examined the effects of transactive memory systems at
the individual
level. For instance, Jarvenpaa and Majchrzak (2008) asked
individuals in an
inter-organizational network, FBI InfraGard program, to
identify the necessary
factors for developing transactive memory when congruent
interests cannot be
assumed. In addition, four studies, including one case study
(Jackson & Klobas,
2008), explored the challenges and possibilities of extending the

concept of
transactive memory system to the organizational level.
To inform our review, we examined the 18 conceptual and
review papers to
identify emerging patterns or missing links in the literature.
Only one paper of
the 18 papers is a review of TMS literature, published by
Peltokorpi (2008) in
the Review of General Psychology, while the rest are thought or
opinion pieces.
Nine of the conceptual papers focused on describing the
working of TMS,
including the two papers by Wegner (1987, 1995), or the
development of
TMS as a result of cognitive interdependence (Brandon &
Hollingshead,
2004), communication (Hollingshead & Brandon, 2003) or
affect (Huang,
2009). Five papers extended the concept to a new level or
context such as an
organizational level TMS (Anand, Manz, & Glick, 1998; Nevo
& Wand,
2005) or emergent groups responding to disasters (Majchrzak,
Jarvenpaa, &
Hollingshead 2007). Three papers discussed TMS as a
mechanism to
manage knowledge integration challenges in virtual teams (e.g.,
Alavi &
Tiwana, 2002). The review by Peltokorpi (2008) summarized
findings of 28
Figure 3 Review of Level of Analysis Published between 1985
and 2010.

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studies at dyadic and team levels and identified three interesting
directions for
future research: the tradeoff between the frequency and quality
of communi-
cation to form TMS, the effects of team diversity on TMS, and
the effects of
conflict and power on TMS. What is missing in these papers is a
comprehen-
sive review of TMS research conducted in different disciplines
and at different
levels of analysis and a navigation map of what we have learned
so far and what
remains unknown, which is what we attempt to deliver in this
paper.
An Integrative Framework of Group Transactive Memory

Systems
Figure 4 summarizes our review of the antecedents,
consequences, and moder-
ating factors of transactive memory in teams and groups
research. The antece-
dents of transactive memory systems that have been studied in
previous
research are depicted on the left side of the figure. The key
components, indi-
cators, and measures of transactive memory systems are
depicted in the middle
of the figure. Consequences of transactive memory systems—
including team
learning, creativity, member satisfaction and, most frequently,
team perform-
ance—are depicted on the right side of the figure. Factors that
have been
studied as moderators of the relationship between transactive
memory
systems and outcome variables are depicted in the lower right
quadrant as
influencing the link between transactive memory systems and
outcome
variables.
We use the Inputs – Mediators – Outcomes framework presented
in
Mathieu et al. (2008) to organize the antecedents and
consequences of
TMS (see also Ilgen, Hollenbeck, Johnson, & Jundt, 2005). We
classify
TMS antecedents into three levels: member attributes or team
composition
Figure 4 An Integrative Framework of Factors Investigated as

Transactive Memory Systems
Antecedents and Consequences.
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inputs, team-level inputs, and organizational-level input. We
classify TMS
consequences into team performance behaviors such as team
learning, team
performance outcomes, and member affective outcomes. We
describe each
section of the framework in detail. At the end of each section,
we identify
factors that need to be analyzed in future research, based on the
Mathieu
et al. (2008) framework, as well as our experiences of
conducting and review-

ing TMS research.
Antecedents to the Development of Transactive Memory
Systems
Based on the team effectiveness framework by Mathieu et al.
(2008), the ante-
cedents of TMS can be classified into three levels: team
composition inputs
(member attributes), team-level inputs, and organizational-level
inputs.
Team composition inputs include member demographics,
competences, and
personality traits. Team-level inputs include interdependence,
group training,
shared experiences, team familiarity, communication (including
computer-
mediated communication), and imposed knowledge structures.
Organizational
inputs investigated thus far include acute stress and the effect of
geographic
distance.
Member demographics and personality traits. Wegner (1995)
described four
bases for attribution of expertise: surface characteristics such as
age, gender,
and ethnicity; assignment; exposure to information; and roles or
an explicit
indication of expertise. Hollingshead and Fraidin (2003)
examined gender
stereotypes and assumptions about expertise and found that (1)
male and
female participants shared similar gender stereotypes across
knowledge cat-
egories and (2) when paired with opposite-sex partners,

participants were
more likely to assign categories based on gender stereotypes
and also to
learn more in categories consistent with stereotypes than in
categories incon-
sistent with stereotypes.
Bunderson (2003) examined two types of member attributes and
how they
were used as status cues to infer member expertise. Diffuse
status cues include
gender and ethnicity. Specific cues include organizational
experience and cer-
tifications. Bunderson (2003) found that both types of cues were
associated
with perceived expertise. The association between specific cues
and perceived
expertise, however, was stronger than the association between
diffuse cues
and perceived expertise. Only specific cues were associated
with intra-group
influence, and only the alignment between specific cues and
intra-group influ-
ence predicted group performance. The difference between the
Hollingshead
and Fraidin (2003) and the Bunderson (2003) studies can be
attributed to
the context: gender is more relevant in intimate relationships
between
couples, and experience and certifications are more relevant in
work groups
in organizational settings.
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We found only one study that examined personality traits of
members in
conjunction with TMS. Pearsall and Ellis (2006) studied critical
team
member dispositional assertiveness and found it to be positively
related to
team performance and team satisfaction. They also found that
TMS mediated
the effects of critical team member assertiveness on team
performance and sat-
isfaction. Assertiveness of critical team members matters
because assertive
members know how to communicate effectively and share ideas
and therefore
can work to facilitate the communication and flow of
information in team
contexts.

Task or reward interdependence. The first paper published on
TMS is titled
Cognitive Interdependence in Close Relationships (Wegner et
al., 1985), which
illustrates the central role of interdependence to TMS.
Interdependence
describes the extent to which team members’ work outcomes
depend on a
combination of their own input and the input of other members.
Cognitive
interdependence develops as group members learn about one
another’s areas
of expertise and become dependent on one another for
acquiring, remember-
ing, and generating knowledge (Hollingshead, 2001). In a
theoretical paper,
Brandon and Hollingshead (2004) presented cognitive
interdependence as
the most critical prerequisite to the development of a TMS.
Cognitive interde-
pendence can result from a reward system or from the structure
of group tasks
and can influence how knowledge domains and responsibilities
are assigned in
groups. In a laboratory experiment, Hollingshead (2001) showed
that conver-
gent expectations for who will learn what were more likely to
develop when
there was a match between cognitive interdependence and
knowledge expected
of a partner. Partners remembered more collectively when they
shared percep-
tions of expertise than when they did not.
We found only one study that empirically tested the role of task
and goal

interdependence on TMS and group performance. Zhang,
Hempel, Han and
Tjosvold (2007) assessed “task interdependence” as the extent
to which team
members believe that they need information, materials, and
support from
other team members in order to carry out their tasks and “goal
interdepen-
dence” as members’ perception of how their goals are related to
other
members’ goals. Although the two are conceptually different,
the researchers
found a positive correlation between task and goal
interdependence (r ¼
0.35). Both task and goal interdependence led to a higher level
of TMS,
which led to improved team performance. Hence, TMS was
found to
mediate between task and goal interdependence, on the one
hand, and per-
formance, on the other.
Group training and shared group experience. Numerous studies
have shown
that groups whose members were trained together developed
more advanced
transactive memory systems and outperformed groups whose
members were
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trained individually (Liang et al., 1995; Moreland, Argote, &
Krishnan, 1996).
Group training facilitates the emergence of TMS for at least two
reasons. First,
it affords members the opportunity to observe one another
performing the
group task and to store cues about each member’s special
expertise. Second,
it affords members the opportunity to communicate, and thereby
to question,
claim, and evaluate one another’s expertise. Rulke and Rau
(2000) coded group
conversation during group training into five categories: (1)
claiming to have
expertise in a certain domain; (2) claiming not to have expertise
in a certain
domain; (3) asking questions concerning a domain of expertise;
(4) planning
and coordinating who is to do what in the group; and (5)
evaluating the exper-
tise and competence of group members. The researchers
observed greater fre-

quency of communication to claim and coordinate expertise in
groups who
were trained together than in those who were trained apart.
Further, groups
with more developed TMS spent more time determining
expertise early on
than groups with less developed transactive memory systems.
The effects of group training on transactive memory systems
and perform-
ance go beyond the benefits from team development or
familiarity. Moreland,
Argote, and Krishnan (1996) conducted an experiment to rule
out explanations
alternative to transactive memory for the enhanced performance
engendered
by group training. The experiment included a condition in which
a team-build-
ing exercise was added after the individual training session (to
increase
member familiarity and encourage group development) and
another condition
in which team members trained with a different group from the
one they per-
formed with (to disable TMS developed in the training session
while preserving
learning acquired during training about how to operate as a
group) as well as
the individual and group training conditions used in the initial
study (Liang
et al., 1995). The researchers found that participants in neither
of these new
conditions developed as strong a TMS or performed as well as
participants
who trained and performed in the same group. The transactive
memory

scores and performance of participants in these two new
conditions did not
differ significantly from the performance of participants who
were trained as
individuals. Further, transactive memory mediated the
relationship between
training and performance, implying that the primary benefit of
group training
was the development of TMS.
Gino, Argote, Miron-Spektor, and Todorova (2010) further
examined the
effects of direct experience on TMS and team creativity. In
three laboratory
experiments, they manipulated direct task experience by having
group
members practice a task similar to what they would be asked to
perform as
a team and indirect task experience by having group members
watch
another team perform the similar task. Compared with indirect
task experi-
ence, direct task experience led to more developed TMS as well
as to a
greater level of creativity. Further, TMS mediated the effect of
experience on
creativity.
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Prior relationships and familiarity among members. Team
familiarity
reflects the extent to which members are knowledgeable about
one another
as a result of prior experiences or interactions. Team familiarity
increases
the likelihood that members become aware of each other’s
expertise or past
experiences. Several studies have shown a positive impact of
team member
familiarity or prior experience on the emergence of a TMS,
especially in
member awareness of expertise location (e.g., Akgun et al.,
2005; He et al.,
2007). For instance, He et al. (2007) studied undergraduate
project teams
and found a positive effect of member familiarity on awareness
of expertise
location. In addition, Littlepage, Robison, and Reddington
(1997) found that
groups of students who had previously worked with the same
group
members on a similar task were better able to recognize member

expertise,
which facilitated group performance.
At the same time, two other studies—by Jackson and Moreland
(2009) and
Michinov and Michinov (2009)—did not show an effect of
member familiarity
on the development of transactive memory systems in student
projects.
Another study, by Lewis (2004) of MBA consulting teams,
showed a moderat-
ing effect of member familiarity on the relationship between
initial expertise
distribution and TMS emergence. A stronger relationship
between distributed
expertise and TMS emergence was observed when familiarity
was high
than low.
To reconcile the divergent findings, we speculate that team
familiarity
affects the dimensions of TMS differently. Knowledge about
other members
from prior experiences is likely to increase general awareness of
expertise dis-
tribution among members (the structural or content component
of TMS),
which may or may not affect TMS processes such as knowledge
specialization,
task credibility, or coordination.
Communication and technology/virtuality. Communication was
among the
first set of factors linked to the development of transactive
memory systems. In
the discussion of transactive memory development in intimate

couples,
Wegner, Giuliano, and Hertel (1985) emphasized the importance
of reciprocal
self-disclosure through which couples reveal information about
themselves to
each other. Hollingshead and Brandon (2003) further elaborated
the role of
communication in helping move individuals from stereotypical
perception of
expertise, based on gender, for example, to sophisticated and
accurate attribu-
tions. Group discussion can provide members with an
opportunity to discuss
and demonstrate their expertise, which in turn allows for greater
precision in
determining who is an expert in a particular knowledge domain.
Pearsall, Ellis, and Bell (2010) studied role-identification
behaviors, when
team members either shared information about their specific
roles or requested
information about their teammates’ areas of responsibility. The
researchers
found that role-identification behaviors were positively related
to the
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emergence of both team mental models and transactive memory,
which
mediated the effects of role-identification behaviors on team
performance.
Several field studies have tested the effect of communication
frequency on
the development of TMS. The effect of communication
frequency has also been
investigated in a simulation (Palazzolo et al., 2006). For
instance, Lewis’ (2004)
study of MBA consulting teams found a positive association
between the fre-
quency of communication during the planning phase and TMS
emergence.
She also found that communication channel mattered in addition
to communi-
cation frequency. Frequent face-to-face communication led to
TMS emer-
gence, but communication via other means such as email and
telephone
conversations had no effect. Similarly, He et al. (2007) studied
undergraduate
project teams and found that the number of face-to-face
meetings and phone

calls had significant and positive effects on awareness of
expertise location, but
emails had no effect.
Despite the general consensus on the importance of
communication for the
development of transactive memory systems, a few
inconsistencies exist in the
literature. One study showed no relationship between team
communication
and TMS (Akgun et al., 2005). Compared with Lewis (2004) and
He et al.
(2007), who examined impromptu student teams, Akgun et al.
(2005)
studied new product teams that had existed for at least six
months. The
reason for the discrepancy may be the time at which team
communication
was measured: in the planning stage, or in the implementation
stage. Kanawat-
tanachai and Yoo (2007) collected three waves of data on
transactive memory
in student projects and found that task-oriented communication
led to exper-
tise location and cognition-based trust two weeks after the
teams were formed,
but the effects disappeared five or eight weeks into the team’s
operation. Simi-
larly, Lewis (2004) found that communication frequency during
the planning
stage facilitated TMS development. Collectively, the studies
suggest that the
effects of communication on the development of transactive
memory are
more valuable in early rather than later stages of group
operations.

Another discrepancy in the literature concerns the effect of non
– face-to-face
communication. Contrary to Lewis (2004) and He et al. (2007),
Kanawattanachai
and Yoo (2007) found a positive effect of computer-mediated
communication on
members’ initial beliefs and trust about others’ specialized
knowledge. We
speculate that the different results regarding the impact of
computer-mediated
communication on TMS development were due to differences in
study contexts.
Both Lewis (2004) and He et al. (2007) studied student teams
that were located in
the same university and thus, could communicate face-to-face.
In contrast,
Kanawattanachai and Yoo (2007) studied MBA students of ten
nationalities
taking courses in four different countries. Each team was
composed of students
from the four countries. There was no chance for members to
engage in face-to-
face communication and members were expected to
communicate only via the
Internet. Consequently, members relied solely upon computer-
mediated
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communication to get work done and were able to develop a
TMS through com-
puter-mediated communication.
To understand the role of communication in transactive
memory, it is also
important to examine communication in the utilization of
transactive memory
(Littlepage, Hollingshead, Drake, & Littlepage, 2008). Research
on the role of
communication in the utilization of shared knowledge is not
entirely consist-
ent. Some studies have shown that communication can facilitate
transactive
retrieval, especially when dyads share information face-to-face,
which allows
them to use nonverbal and paralinguistic cues to signal and
combine their
knowledge effectively (Hollingshead, 1998b). Compared with
members of
face-to-face dyads, members of dyads using the computer were
less likely to
explain their answers and less likely to solicit task-relevant

information from
their partners. Other studies show that groups with a shared
agreement of
member knowledge can coordinate transactive retrieval to
improve perform-
ance without explicit communication (Hollingshead, 2001;
Wegner, Erber, &
Raymond, 1991).
O’Leary and Mortensen (2010) found that the effect of
geographic distri-
bution on the development of transactive memory systems in
virtual teams
depended on the configuration or number of team members at
each location.
Totally dispersed teams with only one member at each
geographic location
developed stronger transactive memory systems than teams with
two or
more members per location. Further, an unequal number of
members per
location led to more negative outcomes than balanced
subgroups.
Written communication and imposed knowledge structure.
Transactive
memory systems can be created artificially, either by assigning
members to
specific domains or by providing members with information
about others’
expertise. Wegner et al. (1991) found that stranger dyads
recalled more
items than natural couples when provided with an explicit
memory assign-
ment. Without the explicit scheme for learning words in
different domains,

intimate couples recalled more words than strangers. The
imposed organiz-
ational structure thus inhibited the performance of intimate
couples because
it interfered with the implicit encoding system they had
developed over the
course of their relationships and yet aided strangers who did not
have such
a system.
Moreland and Myaskovsky (2000) created “artificial”
transactive memory
systems by giving written feedback about members’ specialty to
groups
whose members were trained individually and found those
groups performed
as well as groups whose members were trained together and
better than
groups whose members were trained apart. Similarly, Stasser,
Stewart, and
Wittenbaum (1995) examined the effects of forewarning and
role assignment
on awareness of expertise and decision quality in solving a
hidden-profile
case. Results suggest that simply telling or reminding members
that each has
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distinct information is insufficient to increase expertise
recognition. Specific
information about members’ expertise needs to be provided for
members to
take notice and leverage the information. In addition to
increased expertise rec-
ognition, another advantage of providing specific information
about members’
expertise is enabling the allocation of tasks to match member
expertise (Little-
page et al., 2008). We thus expect such knowledge to affect not
only the struc-
ture of TMS, but also transactive processes such as knowledge
retrieval and task
coordination.
Team environment such as acute stress. Ellis and her
collaborators (2006,
2009) found that acute stress negatively affected transactive
memory processes
such as directory updating, information allocation, and retrieval
coordination,
which mediated the negative effects of acute stress on team
performance. A

later study (Pearsall, Ellis, & Stein, 2009) examined two types
of stressors: chal-
lenge stressors, manipulated as time pressure, and hindrance
stressors, manipu-
lated as role ambiguity. The results show a subtle effect of
different types of
stressors on the development of transactive memory systems.
Although a chal-
lenge stressor led to both more advanced transactive memory
systems and
better performance than no stressor, a hindrance stressor
prohibited the devel-
opment of transactive memory systems and reduced performance
relative to
the no stressor condition.
Summary and future directions. We found considerable work
examining the
effects of communication, group training, team familiarity,
shared experience,
and imposed knowledge structures on the development of
transactive memory
systems. We found a moderate amount of research on how
member demo-
graphics and stress affect TMS development. The effects of
interdependence
and personality characteristics have been investigated in only a
very small
number of studies. Thus, of the three levels of antecedents to
TMS develop-
ment shown in our theoretical framework, team-level inputs
have received sig-
nificantly more attention than member attributes and
organizational-level
inputs. Future research should pay more attention to the effects
of team com-

position inputs such as member diversity (Williams & O’Reilly,
1998) and faul-
tlines (Lau & Murnighan, 1998) and the effects of
organizational context such
as culture and HR practices on TMS development. The former is
important
because it is likely to affect the ease with which members
communicate and
exchange information; the latter is important because it is likely
to affect
members’ motivation to engage in collective processing of
information.
Many of the factors examined as antecedents of TMS, such as
training or
communication, are likely to affect members’ abilities to
develop a TMS and
to a lesser extent their opportunities to do so. Understanding
team
member’s motivation to develop a TMS is also important
(Wittenbaum, Hol-
lingshead, & Botero, 2004). Thus, we encourage additional
research on how
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motivational factors affect the development of transactive
memory systems. A
factor that is likely to affect members’ motivation is the extent
to which group
members identify with the group. In groups where members
identify with the
group, they are more likely to invest in developing the
specialized division of
labor that is a defining characteristic of transactive memory
systems. That is,
members are more likely to divide areas of expertise, rely on
each other, and
share knowledge when they identify with the group than when
they do not.
Future research should examine how motivational factors affect
the develop-
ment of transactive memory systems.
Further research is also needed on factors affecting the
opportunity to
develop a TMS. Social networks can affect the opportunities
members have
to be exposed to each other’s expertise and to develop a shared
division of cog-
nitive labor. For instance, Yuan et al. (2010b) surveyed 218
people in 18 teams
and found a positive relationship between communication tie

strength and
expertise exchange at both the individual and team level. New
technologies
such as “telepresence systems,” whereby members can interact
with others in
geographically dispersed locations, also hold promise for
affording distributed
groups the opportunities to develop a shared cognitive division
of labor.
Another important issue that would benefit from research
attention is the
role of forgetting and the extent to which transactive memory
systems decay.
Because transactive memory systems include knowledge in
individuals’ mem-
ories and the transactive processes that occur between
individuals, understand-
ing knowledge decay involves analyzing individual and
transactive processes.
Concerning individual memories, research on the development
of TMS has
focused primarily on gaining knowledge of who knows what
while at the
same time, members may forget such knowledge if it remains
unused for a sub-
stantial period of time (Ren et al., 2006). Knowledge about who
knows what is
declarative knowledge (know-what) and may decay at a faster
rate than pro-
cedural knowledge or know-how (Cohen & Bacdayan, 1994).
Further,
aspects of an individual’s transactive memory may become
obsolete if
members’ areas of expertise change. For example, a team
member may

acquire new knowledge and skills of which other team members
are not
aware. The decay and obsolescence of transactive memories are
less of a
concern in small and collocated groups where members interact
frequently,
but may be exacerbated in large groups and groups consisting of
geographically
dispersed members.
Concerning the transactive component of transactive memory
systems,
membership change or turnover can disrupt transactive
processes. If a team
member departs, other members will not be able to rely on his
or her expertise.
Networks such as transactive memory systems can also decay
through not
being used. A couple of studies have investigated the role of
turnover in the
utilization of transactive memory systems. When all members of
a team turn-
over from one period to the next, the TMS of the team is totally
disabled and
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thus confers no performance benefits (Moreland, Argote, &
Krishnan, 1996).
When turnover is not total from one period to the next,
transactive memory
systems can retain some value (Lewis, Belliveau, & Herndon,
2007), although
not as much value as when there is no turnover.
Conceiving of transactive memory systems as member – task
links
(McGrath & Argote, 2001) or task – expertise – person links
(Brandon & Hol-
lingshead, 2004) enables us to understand when turnover will be
more or
less harmful in teams. To the extent that the new team member
has expertise
similar to the departing member, turnover will have a less
deleterious effect on
transactive memory systems and subsequent performance. Team
members will
have to learn about the new member’s expertise, but will not
have to reconfi-
gure their transactive structures and processes. As the new
member’s expertise
differs more from that of the departing member, group members
will have to

reconfigure more links in their TMS, learning what they can and
cannot count
on the new member to know, finding alternative sources to the
knowledge that
the new member does not possess, and/or changing their and the
newcomer’s
expertise. In the short term, this will be very disruptive to the
group. In the long
run, the effect of new members with expertise different from
departing
members will depend on the quality of the new member’s
knowledge and
how the group adapts to it. Further work is needed on the
dynamics of trans-
active memory systems, with attention to both how these
systems develop and
how they decay at the level of transactive processes as well as
individual
memories.
Consequences of Transactive Memory Systems on Group
Outcomes
Based on the team effectiveness framework by Mathieu et al.
(2008), the con-
sequences of TMS can be classified into three types: team
performance beha-
viors, team performance outcomes, and member affective
outcomes. Team
performance behaviors are actions that are relevant to achieving
team goals
such as team learning, creativity, and reflectivity. Team
performance outcomes
are the results of performance behaviors such as effectiveness
and efficiency.
Member affective outcomes include member commitment and

satisfaction.
Compared with the antecedents to transactive memory systems,
findings
are much more consistent about the impact of transactive
memory systems
on group outcomes. Numerous studies have shown the positive
effects of trans-
active memory systems on a variety of group outcomes such as
group learning,
team creativity, effectiveness, and member satisfaction (e.g.,
Austin, 2003; Faraj
& Sproull, 2000; Lewis, 2003; Liang et al., 1995; Michinov et
al., 2008). Accurate
expertise recognition improves team performance because it
facilitates the div-
ision of cognitive labor among members, the search and location
of required
knowledge, the match of problems with the person with the
requisite expertise
to solve the problems, the coordination of group activities, and
better decisions
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through the evaluation and integration of knowledge contributed
by group
members (Moreland, 1999).
Group learning, team creativity, and team reflectivity.
Transactive memory
systems have been shown to improve group learning and team
creativity.
Akgun et al. (2006) surveyed Turkish new project development
teams and
found a positive effect of TMS on team learning that was
further linked to
new product success. Similarly, Dayan and Basarir’s (2010)
study of Turkish
new product teams revealed a positive association between
transactive
memory and team reflexivity, conceived as the extent to which
group
members reflect upon the group’s objectives, strategies, and
processes and
adapt them to environmental circumstances. Dayan and Basarir
(2010)
further showed a link from team reflexivity to product success.
In a laboratory
experiment, Gino et al. (2010) found that groups with more
developed trans-
active memory systems demonstrated a higher level of creativity
in creating

products than groups with less developed transactive memory
systems.
Team performance outcomes. When the concept of transactive
memory was
first presented, Wegner (1987) predicted that a group with a
smoothly func-
tioning transactive memory system would be effective in
reaching its goals
and satisfying its members. The statement has received wide
support in labora-
tory experiments, field studies, and simulation models. Two
common tasks in
laboratory experiments are collective remembering and recall of
words from
different domains (Wegner et al., 1991; Hollingshead, 1998a)
and product
assembly (Liang et al., 1995; Lewis, Lange, & Gillis, 2005). It
has been repeat-
edly shown that dyads with transactive memory systems recall
more words col-
lectively and recall more unique or non-overlapping words than
dyads lacking
transactive memory systems (Hollingshead, 1998a, 1998b). It
has also been
repeatedly shown that members trained as a group recalled more
knowledge
about product assembly and made fewer errors than members
who were
trained individually, and that the relationship between training
and perform-
ance was fully mediated by transactive memory systems
(Moreland, Argote,
& Krishnan, 1998). In comparison, transactive memory systems
did not
seem to predict greater speed to complete assembly tasks—at

least, not in lab-
oratory experiments.
In field studies, transactive memory systems have shown
positive associ-
ations with team performance measured in many ways. Faraj
and Sproull
(2000) found that team members’ knowledge of who knows
what (location
or recognition of team expertise) had a positive association with
team effective-
ness assessed by work quality, team operations, ability to meet
project goals,
extent of meeting design objectives, and reputation of work
excellence, as
well as team efficiency measured as time to completion and cost
of the projects.
Rau (2005) found that awareness of expertise location
positively influenced top
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management team performance measured as return on average
assets of banks.
Other studies have linked transactive memory systems to
performance assessed
as new product success, speed to market (Akgun et al., 2005),
decision quality
(Littlepage et al., 2008), and customer service quality
(Peltokorpi, 2004).
Team member satisfaction. The relationship between transactive
memory
and satisfaction was first shown in an experiment of 60
heterosexual couples
who completed a questionnaire (Wegner, Giuliano, & Hertel,
1985). A
couple’s agreement on self- versus other-expertise judgments
was significantly
correlated with each member’s assessment of satisfaction with
the relationship.
The association between transactive memory systems and
satisfaction has also
been confirmed in field studies. Michinov et al. (2008) surveyed
nurse and
physician anesthetists working in French hospitals and found
that higher
scores of TMS were correlated with stronger perceptions of
team effectiveness
and job satisfaction as well as team identification.
Summary and future directions. Team performance measures of
recall and

errors have consistently shown a positive relationship with
TMS. Performance
measures of time to complete tasks have not been associated
with TMS in lab-
oratory studies. Indirect evidence from field studies suggests
that TMS might
reduce the time required to complete tasks. For example,
although they did
not measure TMS directly, Reagans, Argote, and Brooks (2005)
found that
team experience reduced procedure completion time for
orthopedic surgeries.
Compared with team performance outcomes such as assembly
errors and
work quality, there is less research on the effects of TMS on
performance beha-
viors and member affective outcomes. A promising new
direction is the
inclusion of creativity as an outcome. Most groups are
concerned with being
creative as well as being effective and efficient, so including
creativity as an
outcome is a useful development. Yet we found only one study
that examined
the effects of TMS on creativity. Further, investigating the
TMS and creativity promises to open up new avenues in
creativity research,
which examines how knowledge is recombined in new ways
(Kogut &
Zander, 1992). Because groups with well-developed transactive
memory
systems have knowledge of members’ expertise, they are in a
better position
to envision how members’ expertise can be combined in new

ways to create
new products and services than teams with less developed
transactive
memory systems.
Another promising direction to explore is the effects of
performance on the
elaboration of TMS—a feedback loop from team outcomes to
team mediators or
process depicted in the Mathieu et al. (2008) framework. Most
studies we
reviewed are cross-sectional and assume a linear causal
relationship from ante-
cedents to TMS to its consequences. In real life, the connections
may be recur-
sive, and performance could affect the future development of
TMS. Members of
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high-performing teams are likely to maintain and reinforce their
TMS such as
member specialization, whereas members of low-performing
teams are likely to
challenge and modify their TMS by reallocating work
assignments or reducing
their reliance upon others for information. We found only one
study—by Kana-
wattanachai and Yoo (2007)—that investigated the effects of
team performance
feedback on subsequent TMS development. Past performance
reinforced exper-
tise location or members’ metaknowledge of others’ expertise,
but did not affect
trust or task – knowledge coordination. More research,
especially experimental
work with greater control of performance feedback, is needed to
understand
how performance feedback affects TMS development.
Moderators of the Relationship between TMS and Performance
It has been speculated that the effects of transactive memory
systems on group
outcomes vary across group context such as group size, type of
tasks, environ-
mental turbulence, membership change, and geographic
dispersion or technol-
ogies. Alternations in membership (e.g., old-timers leaving or
newcomers
joining) and changes in group tasks or goals disrupt the working
of existing
transactive memory systems and prompt members to reexamine

or renegotiate
their roles, responsibilities, or assumed expertise in the group
(London, Polzer,
& Omoregie, 2005). As a result, these changes can alter the
relationship
between transactive memory systems and group performance.
Group membership change. The group training experiments
showed the
importance of team stability or conversely the detrimental
effects of member-
ship change on transactive memory systems. Teams whose
membership was
totally changed from one period to the next did not perform as
well as
teams whose membership was stable from one period to the next
(Moreland,
Argote, & Krishnan, 1996).
To more fully understand the impact of membership change on
TMS and
group performance, Lewis et al. (2007) conducted a laboratory
experiment
that compared the performance of intact groups, partially intact
groups, and
totally reconstituted groups. The researchers found that
although there was
no significant difference between TMS structure stability in
partially intact
groups and intact groups, partially intact groups had lower TMS
process effi-
ciency scores (i.e., performed worse in utilizing member
knowledge) than did
intact groups, and did not differ significantly from totally
reconstituted groups.
The group’s expertise structure remained largely stable in

partially intact
groups because newcomers adapted and changed their
specializations to sub-
stitute for departing members. Members of partially intact
groups tended to
rely on the TMS structure that old-timers developed in the
original group,
which turned out to be detrimental to group performance
because it created
inefficient TMS processes (Lewis et al., 2007).
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Task change. Lewis et al. (2005) studied the effects of task
change and the
extent to which transactive memory systems developed in one
task generalize

to a similar, yet different, task. The researchers hypothesized
that groups that
had previously developed and utilized a TMS on one task would
perform better
on a subsequent, similar task than would groups with no prior
TMS. Groups
with a prior TMS were more likely to demonstrate abstract,
generalized knowl-
edge about the underlying principles relevant to the task than
groups that had
never developed a TMS. The researchers manipulated task
change by asking a
group to first perform a telephone assembly task and then
perform a stereo
assembly task. Experimental results showed that performance on
the telephone
assembly task was higher in groups with full access to the TMS
they developed
during training. Surprisingly, groups that had developed and
utilized a TMS on
a previous task (the telephone assembly task) did not perform
better on a trans-
fer task (the stereo assembly task). There was evidence,
however, that groups
with an established TMS demonstrated better knowledge of the
underlying
principles of the task domain than those without an established
TMS. A
prior TMS had the most beneficial effect on learning transfer
for members
of groups who had been reassigned for the transfer task and who
had main-
tained either a medium or a high degree of expertise stability
across tasks.
Transactive memory systems in virtual teams. Although only a

few studies
have empirically examined transactive memory systems in
virtual teams
(e.g., see Kanawattanachai & Yoo, 2007; O’Leary & Mortensen,
2010),
several researchers have speculated about the impact of
geographic dispersion
or computer-mediated communication on the development and
utilization of
transactive memory systems. For instance, Alavi and Tiwana
(2002) suggested
that physical distance, technology-mediated interactions, a lack
of antecedent
collaborative history, and the typical diversity in expertise and
backgrounds
of virtual team members are likely to constrain the development
and mainten-
ance of transactive memory in virtual settings. Cordery and Soo
(2008: 491)
identified the failure to develop an effective TMS as a common
barrier to
virtual team success. They further commented: “The boundaries
created by vir-
tuality, in the form of geographic, cultural, and temporal –
spatial separations;
the lack of ‘richness’ in many electronically mediated forms of
communication;
and the fluctuating membership of virtual team structures pose
particular chal-
lenges to team knowledge development and information
sharing.”
Griffith, Sawyer, and Neale (2003) proposed that TMS
development can be
more difficult when groups work apart and are located in
different environ-

ments, because geographic distance reduces the opportunity for
groups to
gain shared experiences, develop common language, or engage
in joint
decision-making (Hollingshead, 1998b). Griffith et al. (2003)
further
proposed that the use of information technologies to facilitate
communication
and sharing of personal information mitigates the negative
relationship
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between working in virtual teams and the development of
transactive memory
systems.

Hollingshead (1998a) suggested that face-to-face
communication plays a
crucial role not only in the development of transactive memory
systems, but
also in their utilization. When working face to face to answer
questions, inti-
mate couples were able to use nonverbal cues to figure out
which partner
knew the correct answer on questions where only one member
was correct
prior to discussion. Compared with strangers who worked face
to face, intimate
couples looked at one another more and worked together more
to remember
information, which improved their performance. Hence,
communication
media affects not only the development of TMS but also the
extent to which
TMS can be leveraged to improve performance.
Group size. Most laboratory studies have examined dyads or
groups consist-
ing of three or four members. There are several field studies
that surveyed
project teams consisting of four or more members, but group
size was only
included as a control variable. Two studies of student project
groups reported
the effects of group size on transactive memory. Michinov and
Michinov
(2009) found that group size was negatively correlated with the
coordination
dimension of transactive memory systems: groups of two
students were able
to coordinate better than groups of three. Jackson and Moreland
(2009)

studied student projects consistent of three, four, or five
members and found
that smaller group size was associated with stronger transactive
memory and
therefore better performance.
The effects of group size on TMS and group performance were
examined
in two simulation studies. Palazzolo et al. (2006) simulated
network sizes of
4 and 20 and found that smaller networks had higher
communication
density and greater TMS accuracy and specialization. Ren,
Carley, and
Argote (2006) simulated group sizes ranging between 3 and 35
and found
that although groups of all sizes benefited from an established
TMS, transactive
memory systems were more beneficial to larger groups in terms
of efficiency or
speed of performance and more beneficial to smaller groups in
terms of
decision quality.
Task type. Although many researchers have speculated that the
effects of
transactive memory on performance vary by types of task
(Lewis &
Herndon, in press), we found only one study that tested the
hypothesis empiri-
cally. Akgun et al. (2005) examined the moderating effect of
task complexity in
new product teams. Task complexity can originate from two
dimensions: repe-
titiveness of a task, and whether the task requires established
bodies of knowl-

edge versus novel solution. Akgun et al. (2005) found that the
impact of TMS
on product outcomes was greater when the task was more
complex (i.e., less
repetitive and requiring more novel knowledge).
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Environmental turbulence. Environmental turbulence, reflected
in changes
associated with new product technologies or the composition of
customers
and their preferences, is likely to render some knowledge
obsolete and thus
alter the effect of an established TMS (Akgun et al., 2006). In a
field study of
new product teams, Akgun et al. (2006) found that greater

environmental tur-
bulence, measured as rapid changes in technologies and
customer preferences,
significantly weakened the relationship between TMS and team
learning as well
as speed-to-market. Ren, Carley, and Argote (2006) also
simulated the impact
of two types of turbulence: task volatility and knowledge
volatility. The
researchers found that while teams in stable environments
outperformed
groups in volatile environments, transactive memory systems
were more ben-
eficial to groups in volatile environments with high task
volatility or high
knowledge volatility. The discrepancy between the two studies
can be attribu-
ted to the level of turbulence. Environmental turbulence in
Akgun et al. (2006)
rendered some team knowledge obsolete and forced the team to
search for new
knowledge to address the change. Consequently, TMS became
less useful with
high environmental turbulence. In contrast, task and knowledge
volatility in
the study by Ren et al. (2006) rendered some individual
knowledge obsolete
and forced the individuals to search for knowledge in the rest of
the team. Con-
sequently, TMS become more valuable with high task or
knowledge turbulence.
Relationship conflict and trust. Rau (2005) examined
relationship conflict
and trust as two moderators of the relationship between
transactive memory

and performance. She defined relationship conflict as
interpersonal incompat-
ibility that includes tension, annoyance, and animosity among
team members
and trust as expectations, assumptions, or beliefs about the
likelihood that
another’s future actions will be beneficial, favorable, or at least
not detrimental
to one’s interests. Survey results show that relationship conflict
negatively
moderated the effects of awareness of expertise location on
performance,
such that awareness of expertise location was not associated
with performance
under high conflict. In contrast, trust was not found to moderate
the effects of
TMS on performance.
Summary and future directions. Changes in tasks, membership,
communi-
cation channels, group dynamics, and environments are all
likely to affect the
working of transactive memory systems and their impact on
performance. A
fruitful direction for future research is to explore managerial
and technological
interventions to assist teams in dynamic settings to function
effectively.
Because members of dynamic organizations move in and out of
project
teams frequently, transactive memory systems at the team level
may be
weaker than those in traditional organizations (Moreland &
Argote, 2003).
Lewis et al. (2007) suggested promising possible interventions
to help team

members cope with membership change. When old-timers of
partially intact
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groups were given information to reflect upon their own
specializations and
the specializations of other members, they proactively adjusted
their specializ-
ations to the addition of a newcomer, which improved the
efficiency of TMS
processes and group performance.
Four Observations and Recommendations for Future Research
We observed interesting patterns in reviewing the literature.
Several suggest

how anomalies in the literature can be reconciled, while others
suggest fruitful
directions for future research. These patterns relate to
conceptual and measure-
ment issues associated with TMS, the level of analysis at which
TMS is studied,
and the promise of TMS for explaining the benefits of
experience in both estab-
lished and new firms. Below, we discuss each of these
observations in turn.
Consistency in measuring TMS
In spite of general convergence in defining the concept of
transactive memory
systems, we observed significant divergence in measuring the
concept. We
identified five sets of established measures of transactive
memory systems in
the literature. One is particular to dyadic studies (Hollingshead,
1998a,
1998b; Wegner et al., 1991) where pairs of participants were
asked to complete
a questionnaire about how well they knew the areas of expertise
of their part-
ners. Four other measures were developed to assess transactive
memory
systems in small groups, including Austin (2003), Faraj and
Sproull (2000),
Lewis (2003), and Liang et al. (1995). Among those, the
measures of Austin
(2003) and Faraj and Sproull (2000) have been used primarily in
field
studies. The Liang et al. measurement approach (1995) has been
used primarily
in laboratory experiments. The Lewis (2003) measure can be

adapted to use in
both the laboratory and the field.
Among these measures, Liang et al. (1995) was the first to
assess TMS in the
context of small groups. The researchers identified three
behavioral indicators
of TMS as memory differentiation (tendency for members to
specialize in
remembering distinct knowledge), task credibility (how much
members trust
each other’s knowledge), and task coordination (ability of
members to work
together smoothly). Judges who were blind to the research
hypotheses
watched videotapes of groups working together and rated the
extent to
which the groups demonstrated the three behaviors. In addition,
participants
individually completed a questionnaire with items about their
own and other
members’ expertise, which were used to calculate three direct
measures of
TMS as complexity, accuracy, and agreement among members
about others’
expertise. The correlations between direct and indirect,
behavioral measures
were positive and significant (Moreland, 1999).
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The Austin (2003) and Lewis (2003) measures were developed
on the basis of
Liang et al. (1995) to be used in field studies. Austin (2003)
built on the direct
measures and included four components: knowledge stock
(combination of
individual knowledge), consensus about knowledge sources
(agreement),
specialization of expertise, and accuracy of knowledge
identification. Lewis
(2003) built on the behavioral measures and included three
dimensions: knowl-
edge specialization, task credibility, and task coordination.
Table 1 shows the
three measures by Lewis (2003), Austin (2003), and Faraj and
Sproull (2000).
Among the established measures, Lewis (2003) has been the
most widely
adopted. As shown in Table 1, the Austin (2003) measure is
labor intensive
for both researchers and respondents. To construct the

questionnaire,
researchers need to conduct preliminary interviews to identify
all skills or
areas of knowledge that are relevant to the team being studied.
For each skill
or area of knowledge, respondents rate the ability level of all
members. For
example, for a team with 10 members and 12 skills, each
respondent is required
to fill out 10 × 12 ¼ 120 cells. Decisions about which measure
to use depend,
of course, on study goals and the empirical context. An
advantage of the Lewis
(2003) measure is that it can be used in studies that include
different kinds of
groups. By contrast, the Austin measure is constructed based on
detailed
knowledge of the skills and knowledge that is relevant in a
particular group.
Its fine-grained assessment of knowledge and skills enables one
to determine,
for example, where perceptions of expertise are more or less
accurate, which in
turn can enable interventions to strengthen TMS.
Our review suggests convergence in using standard measures,
especially
Lewis (2003), to assess TMS. Out of the 31 field studies we
reviewed, 27
included measures of TMS (two were case studies and the other
two did not
provide details of how they measured TMS). Among the 27
studies, 12 used
the 15-item scale developed by Lewis (2003), three used the
expertise location
and bringing expertise to use measure by Faraj and Sproull

(200), and one used
the Austin (2003) measure. The remaining 11 studies used self-
developed
measures that do not correspond well to the established
measures. Using stan-
dard measures is valuable in promoting the accumulation of
knowledge. If
researchers believe that the standard measures are not
appropriate for their
theory or empirical context and want to develop new measures,
we advocate
using a standard measure in addition to the new measure and
comparing
the two measures. Such comparisons can lead to refinements or
elaborations
of measurement approaches and enable the field to progress by
promoting
the accumulation of knowledge.
TMS as a multidimensional construct
In spite of consensus about the multidimensional nature of
transactive
memory systems, there is great divergence in the
conceptualization of the
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Table 1 Established Measurements of Transactive Memory
Systems
Lewis (2003) measures
Specialization
Each team member has specialized knowledge of some aspect of
our project.
I have knowledge about an aspect of the project that no other
team member has.
Different team members are responsible for expertise in
different areas.
The specialized knowledge of several different team members
was needed to
complete the project deliverables.
I know which team members have expertise in specific areas.
Credibility
I was comfortable accepting procedural suggestions from other
team members.
I trusted that other members’ knowledge about the project was
credible.
I was confident relying on the information that other team

members brought to the
discussion.
When other members gave information, I wanted to double-
check it for myself.
(reversed)
I did not have much faith in other members’ “expertise.”
(reversed)
Coordination
Our team worked together in a well-coordinated fashion.
Our team had very few misunderstandings about what to do.
Our team needed to backtrack and start over a lot. (reversed)
We accomplished the task smoothly and efficiently.
There was much confusion about how we would accomplish the
task. (reversed)
Faraj & Sproull (2000) measures
Expertise location
The team has a good “map” of each other’s talents and skills.
Team members are assigned to tasks commensurate with their
task-relevant knowledge
and skill.
Team members know what task-related skills and knowledge
they each possess.
Team members know who on the team has specialized skills and
knowledge that is
relevant to their work.
Bring expertise to bear
People in our team share their special knowledge and expertise
with one another.
If someone in our team has some special knowledge about how

to perform the team
task, he or she is not likely to tell the other member about it.
There is virtually no exchange of information knowledge or
sharing of skills among
members. (reversed)
More knowledgeable team members freely provide other
members with hard-to-find
knowledge or specialized skills.
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relationships among its dimensions. Two common approaches
are to either
combine all dimensions into one construct or to examine each

dimension
as a separate construct and relate it to criterion variables. The
first approach
was started by Liang et al. (1995) and reinforced by Lewis
(2003). Typically,
the authors concluded that because the three behavioral
measures of
transactive memory systems or the three dimensions were
strongly corre-
lated, their scores were averaged to create a composite index to
measure
TMS as a whole. Lewis and Herndon (in press) further defined
specialization,
credibility, and coordination as manifestations of a latent TMS
variable,
meaning that (1) when a TMS exists, it causes specialized
knowledge,
mutual trust in others’ knowledge, and smooth, coordinated task
processing;
and (2) specialization, credibility, and coordination co-vary
because they
have a common cause.
Another less common approach is to examine the dimensions
and relate
them to criterion variables separately. The implication is that
the dimensions
of transactive memory systems are not always highly correlated
with one
Table 1 Established Measurements of Transactive Memory
Systems (Continued)
Lewis (2003) measures
Austin (2003) measures

Group task knowledge stock
Preliminary interviews to identify all skill/knowledge areas.
Each group member is
required to rate their ability and other members’ ability in all
skill/knowledge areas
using a 5-point Likert scale (a matrix of P × N with P equal to
the number of members
in the group and N equal to the number of skill/knowledge
areas).
Task knowledge specialization
Take the P × N matrix. Count the number of times each person
is identified as an expert
(a number ranging from 0 to N). The standard deviation of
counts across skills for each
individual is the person’s knowledge specialization score.
Transactive memory consensus
Take the P × N matrix. For each skill, write the name of the
team member that you
believe is most knowledgeable. For each skill, check the
frequency at which a person is
named as the most knowledgeable member and assign 1 to the
most frequently
identified expert, 2 to the next most frequently, and so on. The
standard deviation for
each skill indicates group consensus on identification of experts
for that skill.
Transactive memory accuracy
Self-report expertise scores (e.g., Person B reported a skill level
of 5 in area 1) are linked

with identified expert scores (e.g., Person A rated Person B as
the expertise in area 1) to
create accuracy score for each individual and skill (therefore
Person A’s accuracy of
skill area 1 is 5). Skill accuracy scores are averaged to an
individual accuracy score.
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another to justify their combination. For instance, Austin (2003)
found the
strongest correlation between consensus and specialization
(0.65), probably
because both develop as group members gain experience
working with each
other. Accuracy was correlated with consensus (0.34) and
specialization

(0.18), but not as strongly as the correlation between consensus
and specializ-
ation. Further, Austin (2003) found that transactive memory
accuracy was the
most significant predictor of group performance, knowledge
specialization was
related to external and internal evaluations, and consensus was
not signifi-
cantly related to performance.
Table 2 shows correlations between the dimensions of TMS in
three field
studies. As can be seen from the table, the magnitudes of the
correlations
vary from no correlation to moderate correlation. A key
difference between
these studies and the many studies that have reported high
correlations
among the dimensions is the type of groups and length of time
groups have
worked together. The studies shown in the table examined
mature, established
teams in organizational settings such as product teams, hospital
teams, and
software development teams. Members of these groups often
developed their
specializations in another context through training, education,
or prior
project experiences. These groups can still vary greatly in the
recognition of
expertise, trust of each other’s expertise and coordination. The
correlation
between the dimensions of transactive memory systems,
however, may not
be as strong as in newly created groups where specialization,
credibility and

coordination develop together.
Researchers have also speculated that the dimensions of
transactive
memory might follow different evolutionary paths. For example,
Brandon
and Hollingshead (2004) proposed three stages of transactive
memory devel-
opment. They suggested that a benefit of group interaction is
the development
of perceptions of expertise over time from crude perceptions
based on surface
characteristics, such as gender, to more accurate perceptions of
group member
expertise. Knowledge of what others know first becomes
complex (gaining the
knowledge or making inferences from individual
characteristics), then accurate
(modifying and refining knowledge to match reality), and
finally shared or
Table 2 Correlations among TMS Dimensions
References
r(specialization,
credibility)
r(specialization,
coordination)
r(credibility,
coordination)
Akgun et al. (2005) 0.26 (p , 0.05) 0.29 (p , 0.05) 0.30 (p , 0.01)
Faraj & Sproull (2000) 0.31 (p , 0.05) 0.17 (N.S.) 0.56 (p ,

0.01)
Michinov et al. (2008) 0.15 (N.S.) 20.005 (N.S.) 0.41 (p , 0.01)
Note: We equated expertise presence in Faraj & Sproull (2000)
to specialization in Lewis (2003),
expertise location to task credibility, and bring expertise to bear
to task coordination. N.S., not
significant.
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convergent (through validation). According to Brandon and
Hollingshead
(2004), the optimal state of a transactive memory system is
convergence
where all members have similar representations of the TMS that
accurately

reflect relative knowledge in the group.
We recommend that future studies report not only TMS as a
construct, but
also the value of different dimensions and their
intercorrelations. Most studies
we reviewed did not provide such statistics, which makes it hard
to compare
across contexts.
Another reason to think more deeply about the relationships
among the
dimensions is that several researchers have proposed and found
that different
dimensions have different levels of influence on performance.
For example,
Michinov et al. (2008) found a high contribution of
coordination to team effec-
tiveness and satisfaction, a small contribution of credibility, and
no effect of
specialization. Kanawattanachai and Yoo (2007) found that
expertise location
and cognition-based trust affected performance through task –
knowledge
coordination. The researchers also observed that the three
dimensions of
TMS did not follow the same evolutionary path, with expertise
location and
cognition-based trust being stable once initially formed and task
– knowledge
coordination being more dynamic and emergent.
We agree with the position of Lewis and Herndon (in press) that
one needs
some degree of both specialization and coordination for a TMS
to exist. What

is unique about the concept of transactive memory is that it
combines special-
ization and coordination: not only do members of groups with
well-developed
transactive memory systems specialize in remembering different
knowledge,
they also know who knows what and coordinate their knowledge
effectively.
The dimensions of specialization and coordination correspond
to the original
theorizing of Wegner et al. (1985) about the two components of
transactive
memory systems: knowledge in individual memories, and the
transactive pro-
cesses that link the knowledge in individual memories.
Examining the separate
as well as the interactive effects of specialization and
coordination on task per-
formance and the conditions under which each variable is
positively related to
task performance will advance our understanding of transactive
memory
systems.
The role of trust in the concept of transactive memory systems
needs to be
sharpened. Trust has been conceived as an antecedent (Akgun et
al., 2005) or
dimension (Kanawattanachai & You, 2007) of transactive
memory systems as
well as a moderator of the relationship between transactive
memory systems
and performance (Rau, 2005). Trust itself is a
multidimensionsal construct
(McEvily, in press). It can be based on assessment of different
characteristics

of a trustee such as ability, benevolence, and integrity (Mayer et
al., 1995) or
different sources such as knowledge about the trustee
(cognition-based) or
emotional bonds with the trustee (affect-based) (McAllister,
1995). The type
of trust that is most closely related to transactive memory
systems is knowledge
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of and trust in member’s expertise or what researchers have
termed task credi-
bility (Liang et al., 1995; Lewis, 2003). Our review suggests
that the relationship
between trust and TMS is complicated and requires further
research.

An organizational-level TMS
The current literature focuses primarily on teams and how
transactive memory
systems develop and affect team performance. There is very
limited research on
the presence or impact of transactive memory systems at the
organizational
level. Our review suggests that the concept can be generalized
to the organiz-
ational level, yet the generalization requires substantial
elaboration to the theory.
Several researchers have written about the challenges of
generalizing TMS
to the organizational level. First, organizations are usually
larger than work
groups, so their transactive memory systems should contain
more (and more
varied) sources of task knowledge (Moreland, 1999). As a
result, members
might have more trouble identifying who knows what in
organizations than
in small work groups. Second, organizations consist of multiple
subgroups
with less communication and knowledge sharing across
subgroup boundaries
(Anand et al., 1998). Third, organizations are increasingly
geographically dis-
tributed and rely upon advanced technologies to locate and
share information.
Fourth, it is easier to share tacit knowledge in a small group,
but when the tacit
knowledge is available in a distal part of the organization,
retrieval becomes

challenging (Nevo & Wand, 2005).
How do transactive memory systems operate in organizations?
The options
can be divided into two broad categories (Moreland, 1999):
interpersonal and
technological. The general goal of the interpersonal approach is
to locate a
specific person in the organization and obtain whatever
information is
needed from him or her. The general goal of the technological
approach is
to obtain whatever information is needed through the use of
tools such as com-
puters. The latter approach can be elaborated into two forms:
knowledge pro-
vided through repositories, or knowledge provided by
connections mediated
through computers. Although empirical evidence on the effects
of knowledge
repositories on organizational performance is mixed (Haas &
Hansen, 2005;
Kim, 2008), repositories and directories have the potential to
build transactive
memory systems by providing pointers to who knows what in
the organization.
With old technologies such as Intranet and corporate “yellow
pages,”
employees are often reluctant to use these technologies because
they need to
regularly upload their latest CVs or list their recent projects to
keep the infor-
mation up to date. Current approaches to knowledge
management such as
those enabled by Web 2.0 provide the capability to connect

people as well as
to capture knowledge in repositories. Further, the
communication capabilities
afforded by Web 2.0 technologies such as wikis, blogs, and
online social net-
works have the potential to automatically populate directories
of who knows
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what while members edit their personal profiles or post
documents about their
work activities (Majchrzak, Cherbakov, & Ives, 2009).
Companies can install
tools to mine emails, instant messaging, and virtual space usage
to automati-
cally infer expertise and identify social networks. Not only does

the system
provide information about who knows what, it can also display
social networks
for a particular topic and calculate degrees of separation
between the employee
and all others in their network, so that the employee can find a
route to reach a
remote source of information through direct contacts. Another
useful tool for
knowledge integration is a wiki, a website that anyone can edit.
The name wiki
came from the Hawaiian word meaning “quick.” Wikis are used
typically by
multiple users to codify their knowledge and co-generate useful
content. As
employees communicate and collaborate using these new
technologies, the
records can be automatically analyzed to derive information
about who
knows what and who is interested in what. Thus, these new
technologies
have “affordances” that enable the development of transactive
memory
systems without members being in the same location. The
affordances
provide opportunities (Zammuto, Griffith, Majchrzak,
Dougherty, & Faraj,
2007). Whether the opportunities are realized depends on how
the tools are
used, a topic on which additional research is needed.
Jackson and Klobas (2008) conducted a case study to identify
TMS processes
in organizations and explore ways to design systems to improve
the functioning
of organizational TMS. In terms of directories and directory

maintenance, they
found organizational members employed a variety of directory
structures,
informal and formal, internal and external, primitive and high-
tech, which
pointed them to information sources or “someone who might
know
someone.” Managers and administrators functioned as keepers
of directories
or gatekeepers. In addition, two organization-level information
systems sup-
ported directories: a portal through which employees could
report their activi-
ties and search a curriculum vitae database, and a file
management system from
which directory information could be obtained by identifying
the individuals
who wrote documents on various topics. The researchers noted
that physical
separation led to fewer opportunities to maintain directories of
“who knows
what” in the organization. Being given information by others
was a minor
source of knowledge in the case study organization. Knowledge
was gained
mostly on the job and by one’s own research. Compared with
members of
small groups, members of a large organization seem less
motivated and have
less opportunity to actively allocate or direct knowledge to
relevant experts.
The rotation of individuals throughout the organization can be
an effective
mechanism for building an organizational-level TMS. Such
rotation exposes

members in recipient groups to the knowledge and skills of
rotating
members, and thereby spreads knowledge of individuals’
capabilities through-
out the organization. The rotation of individuals is especially
likely to spread
knowledge when organizational members share a strong
superordinate identity
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(Kane, Argote, & Levine, 2005). Further, member rotation is a
mechanism for
spreading tacit as well as explicit knowledge (Berry &
Broadbent, 1984, 1987).
Although member rotation can disrupt the operation of a
group’s TMS, the

disruption can be minimized by keeping the proportion of
rotated members
small and by providing the group with information about the
new member’s
knowledge and skills (Lewis et al., 2007).
In addition to knowing and trusting others’ expertise, studies
have shown the
importance of accessibility or cost of access in facilitating
information seeking
and sharing in organizations. Borgatti and Cross (2003)
suggested that the like-
lihood of seeking information from another person is a function
of knowing and
valuing what the person knows, getting timely access to that
person, and the
perceived cost of the information-seeking effort. Similarly, in a
field study of
hospital nurses, Hofmann, Lie, and Grant (2009) found that the
decision to
seek help from others depended on help-seekers’ perceptions of
experts’ acces-
sibility and trustworthiness. In a study of a geographically
distributed sales
team, Yuan et al. (2010a) found that expertise retrieval was
affected by aware-
ness of expertise distribution and social accessibility. Although
these studies do
not explicitly discuss an organizational-level TMS, they do
illustrate the
working of the expertise retrieval process at the organizational
level.
Overall, the concept of transactive memory systems shows
promise for
being generalized to the organizational level, although our

understanding of
how an organizational TMS functions remains limited. How an
organizational
TMS functions and manifests, with the assistance of social
networks and infor-
mation technologies, is a fruitful future research direction.
The relationship between group and organizational transactive
memory
systems also offers a promising direction for future research.
Do strong trans-
active memory systems at the group level of analysis that are
linked through
social mechanisms as well as technological mechanisms enable
a strong trans-
active memory at the organizational level? For example,
building strong trans-
active memory systems at the group level of analysis and
linking groups
through a small world network could enable a strong TMS at the
organiz-
ational level of analysis. The simulation results of Fang, Lee,
and Schilling
(2010) about organizational learning support this conjecture.
Alternatively, do strong transactive memory systems at the
group level of
analysis interfere with building a strong TMS at the
organizational level of
analysis? To the extent that factors that facilitate building a
strong transactive
memory at the group level of analysis, such as membership
stability, are nega-
tively related to the factors that facilitate building a strong TMS
at the organiz-
ational level of analysis, such as member rotation across groups,

a negative
relationship between the strength of transactive memory
systems at group
and organizational levels can develop. If such a negative
group and organizational-level transactive memory systems
exists, we suggest
focusing on building a TMS at the level of analysis where task
interdependence
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is reciprocal (Thompson, 1967) and timing of the articulation of
team member
skills and knowledge is most critical. For example, in situations
where task inter-
dependencies are very strong within groups and weaker between

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